In today's technologically-driven society, there exist various technical processes and systems for aggregating and sorting data to assist in the determination of the performance of one individual when compared to another, the performance of one device when compared to another, or the performance of anything when compared to another thing. Currently, for example, there exist large databases of athletic data, which include combine measurements for various exercises that athletes participate in during one or more combine events. Such data may include, but is not limited to, the athlete's name and demographic information, along with the athlete's specific raw measurements for each of the various exercises that a particular athlete has participated in. While recruiters of athletes can attempt to sift through such large databases of athletic data to determine the optimal candidates for their programs, such large databases are often unwieldy and difficult to navigate through with existing technologies. As a result, current recruiters often resort to ranking athletes based on word-of-mouth or by viewing substantial amounts of game film. While top athletic recruiters at top athletic programs do a decent job of finding higher tier athletes for their athletic programs, they still struggle in finding the optimal candidates that have the optimal skills for selected positions of a particular sports team. Additionally, lower tier athletic programs often struggle in finding quality candidates because of a lack of funding, a lack of time, and a lack of access to any sort of centralized and valid information databases.
While various types of data aggregation and sorting technologies and processes exist today, such technologies and processes still have many shortcomings. For example, even though athletic data is often available for various athletes, such data is often invalid or skewed due to the exaggerations made by high school coaches for the benefit of the athletes entering the recruiting process. Additionally, current technologies and processes do not readily allow recruiters to target specific traits and characteristics of athletes in an efficient manner. Furthermore, current technologies and processes often place complete control and analysis of the data with the proprietor of the system handling the data. Moreover, while current technologies have attempted to create indices of athletic measurables, such indices are created using standardized formulas which are fixed by a particular organization and do not allow for customization. As a result, current data aggregation, data sorting, and data transformation technologies and processes may be modified and improved so as to provide enhanced functionality and features for users and companies. Such enhancements and improvements to data aggregation, data sorting, and data transformation technologies and processes may provide for improved user satisfaction, increased efficiencies, increased access to meaningful data, substantially-improved decision-making abilities, and increased ease-of-use for users.